The Science Behind Grounded AI Answers
ChatEaver uses Retrieval-Augmented Generation (RAG) to ensure your AI assistant answers visitor questions using only your verified business facts. Unlike traditional AI models that guess or invent answers based on general internet data, ChatEaver retrieves relevant passages directly from your Knowledge Base before generating a response.
The 4-Step Ingestion & Retrieval Pipeline
Pipeline Stage | Process Description | Outcome |
|---|---|---|
1. Parsing & Cleaning | Extracts clean text content from PDFs, plain text files, web pages, and FAQs. | Raw text stream without layout clutter. |
2. Chunking | Splits long text documents into semantic chunks (e.g. 500-1000 characters). | Manageable, contextual passages. |
3. Vectorization | Generates high-dimensional vector embeddings for each text chunk. | Searchable semantic database index. |
4. Context Injection | Finds top matching chunks for visitor queries and injects them into the AI prompt. | Accurate, grounded response generation. |
Supported Knowledge Sources
PDF Documents: User manuals, policy sheets, technical specifications, and catalogs.
Plain Text & FAQs: Direct question-and-answer pairs and concise factual statements.
Scraped Web Pages: Public website URLs and XML sitemaps automatically crawled for content.
Cloud Storage & Integrations: Google Drive, Notion, and external document connectors.
Best Practices for High Retrieval Accuracy
Keep information current: Remove outdated pricing or legacy product versions.
Use clear headings: Organize documents with distinct section titles and bullet points.
Avoid internal jargon: Write facts using the terms your customers naturally type.
Separate conflicting policies: Ensure distinct plans or regions do not contradict each other in separate files.